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15th International Conference on computer and automation engineering, ICCAE 2023

作者： Kuric, Ivan Stenchlak, Vladimir Zajacko, Ivan Bohusik, Martin Wiecek, Dorota University of Žilina Department of Automation and Production Systems Žilina Slovakia University of Bielsko-Biala Faculty of Mechanical Engineering and Computer Science Bielsko-Biala Poland

ISBN: (纸本)9798350396225

This article deals with the problematics of obtaining and processing the EOG signals. This research describes the ways in which it is possible to measure and evaluate the measured bio-electrical signals of brain and muscle activity in more detail. The work also describes the analysis of the current state of this problematics and individual models of artificial intelligence are also described in the work - artificial neural networks that can be used for such advanced analysis. The experimental part of this work is performed in the MATLAB environment using the Deep Neural Network Toolbox. The experimental part of the work is focused on the analysis and processing of bio-electrical activity eye-motor muscles in the area of eye-tracking with the help of the OLIMEX EEG-SMT device. The information provided in this research can be used for improving safety and reliability and elimination of potential risks connected to an epilepsy disease and epilepsy seizures in automated production systems. © 2023 IEEE.

关键词： Deep neural networks

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Pareto Analysis of PI Tuning in Direct Digital Control of Multi-phase Drives ⁎

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IFAC-PapersOnLine 2024年第7期58卷 144-149页

作者： Manuel G. Satué J.D. Álvarez Manuel A. Perales Systems Engineering and Automation Department University of Seville 41092 Seville Spain Department of Computer Engineering Automation and Robotics CIESOL—ceiA3 University of Almería 04120 Almería Spain Electronic Engineering Department University of Seville 41092 Seville Spain

PI tuning for variable-speed multi-phase drives is a complex task. In this paper, a Pareto analysis is introduced to reveal not previously reported links between figures of merit. The drive used for the study includes a 5-phase induction motor supplied by a voltage source inverter. A finite state predictive method is used for the inner loop (current control). The outer loop (speed control) is governed by a PI. The analysis is done experimentally thus including all sorts of non-idealities not appearing in commonly found models. The experimental results show how the pursuit for better performance is hindered by the existence of links between figures of merit. The importance of the result lies in showing that arbitrary performance enhancements are not possible in a general case.

关键词： Application of power electronics Digital implementation Modeling simulation of power systems PI control Tuning

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Computationally Efficient VBR-Based Sensitivity Analysis of Induction Motors

Computationally Efficient VBR-Based Sensitivity Analysis of ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Koti, Hossein Nejadi Valipoor, Zahra Demerdash, Nabeel A. O. Rockwell Automation Inc. Motion Control Team Mequon United States Marquette University Electrical and Computer Engineering Department Milwaukee United States

ISBN: (纸本)9798350316445

Sensitivity analysis is a powerful tool that can be utilized for reducing the size of the design space at hand to explore and significantly reduce the computational burden of the optimization process. This is achieved by 'smart' design variable selection. The term 'smart' indicates fixing the un-conflicting design variables that do not require exploration to their maximum or minimum values, depending on their enhancing or degrading effect on the objective functions and allowing the conflicting design variables to be explored within their allowable dynamic range. Intelligent optimization is a critical part of induction motor design process. In this paper, computationally efficient sensitivity analysis is performed to effectively reduce the number of the design variables that will be actively explored in the optimization process of the induction motors. This goal is achieved through the VBR-based TS-FEA together with Design of Experiment and Response Surface Methodology. In addition, the motor estimated losses, including the calculated core losses are fed to a thermal network model to estimate the temperature of different motor components. © 2023 IEEE.

关键词： Core Losses Induction Motor Temperature Thermal Calibration VBR-based FEA

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A NOVEL REAL-TIME DEVS SIMULATION ARCHITECTURE WITH HARDWARE-IN-THE-LOOP CAPABILITIES

A NOVEL REAL-TIME DEVS SIMULATION ARCHITECTURE WITH HARDWARE...

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2024 Annual Modeling and Simulation Conference, ANNSIM 2024

作者： Fernández-Sebastián, Óscar Cárdenas, Román Arroba, Patricia Risco-Martín, José L. Department of Computer Architecture and Automation Universidad Complutense de Madrid Spain Department of Electronic Engineering Universidad Politécnica de Madrid Spain Center for Computational Simulation Universidad Politécnica de Madrid Spain

ISBN: (纸本)9781713899310

Advanced Modelling and Simulation (M&S) techniques, including Digital Twins (DTs) and Hardware-In-the-Loop (HIL) simulation, are essential for the design and operation of today’s complex systems. Despite these advances, there remains a gap between the simulation methods used in design and those used in implementation. This paper introduces a Real-Time (RT) simulation framework that connects models using the Discrete-Event System Specification (DEVS) with external systems, improving implementation and operation. The Python xDEVS tool has been enhanced to introduce a new suite of real-time simulation components and a DEVS-based communication interface, facilitating adaptable communication protocols. The framework demonstrates its capability to integrate both DEVS and non-DEVS systems and establish effective communication through various experiments, ranging from RT simulation of DEVS models to complex multi-component systems. This tool has significant potential in supporting real and incremental system implementations through HIL techniques, making a valuable contribution to complex system design and operation. © 2024 Society for Modeling & Simulation International (SCS).

关键词： Hardware-in-the-loop simulation

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Evaluation of Convolutional Neural Network Model Architecture Performance

Evaluation of Convolutional Neural Network Model Architectur...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Pradana, Musthofa Galih Khoirunnisa, Hilda Pinastawa, I Wayan Rangga Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia Bandung Polytechnic for Manufacturing Engineering and Mechatronics Department of Automation Bandung Indonesia

ISBN: (纸本)9798350307306

The identification of image types can be facilitated through the use of algorithms and automation in their detection. In this research, detection is performed on fashion images, which encompass various types and categorizations. The process of identification and detection can utilize the Neural Network algorithm known as Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) algorithm consists of multiple layers, and its application involves setting values for pixels as well as arranging the sequence of layers within it. In this study, two Convolutional Neural Network (CNN) algorithm models are employed for image classification in the fashion dataset. These two models differ in the order of layers within the Convolutional Neural Network (CNN) and in the values of strides applied for modification. Model 1 employs ZeroPadding2D in the first layer with a smaller stride value of 1 compared to Model 2, which places the Conv2D layer in the first layer and applies strides with 1, 2, and 3 values. This research indicates that Model 1 outperforms Model 2, achieving the highest accuracy in epoch 20 with an accuracy value of 0.9711. This is attributed to the fact that the strides value in the Conv2D layer of Model 1 is set to a smaller value compared to Model 2. © 2023 IEEE.

关键词： Classification (of information)

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UBER: An Unreal Engine Based Simulation Platform with Extensibility and Real-time Capability*

UBER: An Unreal Engine Based Simulation Platform with Extens...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Chen, Jizheng Shi, Zhan Wang, Xuyu Wu, Jing Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Jiao Tong University Department of Civil Engineering China Shanghai Jiao Tong University Department of Microelectronics Science and Engineering China Shanghai Jiao Tong University Department of Automation China

ISBN: (纸本)9798350327182

In this paper, we propose a real-time platform called UBER, which stands for Unreal Engine Based simulation platform with Extensibility and Real-time capability. It provides a visualization way to the online tests of unmanned vehicle simulation with Unreal Engine 5 (UE5). By building a TCP communication module to exchange message between two ends in UBER, we successfully solve the problem that no code script can be operated in blueprints of UE5. Excellent physics components in UE5 makes simulation visualization more real and beautiful compared with other Reinforcement Learning environments, and by accessing the python scripts outside of blueprints, various of elements, such as scenario components and environmental settings, etc., can be easily modified. Furthermore, a series of reinforcement learning and formation control algorithms are implemented on UBER to show its extensibility and real-time capability, proving that our TCP communication method brings forth a good solution to the problem that UE5 does not support python scripts within blueprint. © 2023 IEEE.

关键词： Simulation platform

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High-performance and Multi-functional Control of Transformerless Single-phase Smart Inverter for Grid-connected PV System

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Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1386-1394页

作者： Kamran Zeb Saif Ul Islam Waqar Uddin Imran Khan Muhammad Ishfaq Tiago Davi Curi Busarello H.J.Kim Department of Electrical Engineering Pusan National University School of Electrical Engineering and Computer Science National University of Sciences and Technology Department of Control Automation and Computing EngineeringFederal University of Santa Catarina Blumenau

Highly reliable and flexible control is required for distributed generation(DG) to efficiently connect to the *** inverters play a key role in the control and integration of DG into the power grid and provide advanced functionalities. In this paper, an energy-based single-phase voltage-source smart inverter(SPV-SSI) of 5 k VA is designed and analyzed in detail. SPV-SSI is capable of supplying the power to local load and the utility load up to the rated capacity of the inverter, injecting the power into the grid, storing the energy in lead-acid battery bank, controlling the voltage at the point of common coupling(PCC) during voltage sags or faults, and making decisions on real-time pricing information obtained from the utility grid through advanced metering. The complete design of smart inverter in dq frame, bi-directional DC-DC buck-boost converter, IEEE standard 1547 based islanding and recloser, and static synchronous compensator(STATCOM) functionalities is presented in this paper. Moreover, adaptive controllers, i. e., fuzzy proportional-integral(F-PI) controller and fuzzy-sliding mode controller(F-SMC) are designed. The performances of F-PI controller and F-SMC are superior, stable, and robust compared with those of conventionally tuned PI controllers for voltage control loop(islanded mode) and current control loop(grid-connected mode).

关键词： Distributed generation(DG) inverter convert-er fuzzy-sliding mode controller(F-SMC) fuzzy proportionalintegral(F-PI)controller

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Iterative Noisy-Target Approach: Speech Enhancement Without Clean Speech 18th

Iterative Noisy-Target Approach: Speech Enhancement Without ...

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Zhang, Yifan Jiang, Wenbin Zhuo, Qing Yu, Kai MoE Key Lab of Artificial Intelligence X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Communication Engineering Hangzhou Dianzi University Hangzhou China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9789819706006

Traditional Deep Neural Network based speech enhancement usually requires clean speech as the target of training. However, limited access to ideal clean speech hinders its practical use. Meanwhile, existing self-supervised or unsupervised methods face both unsatisfactory performance and impractical source demand (e.g., various kinds of noises added to the same clean speech). Hence, there’s a significant need to either release the restriction of training data or improve the performance. In this paper, we propose a training strategy that only requires noisy speech and noise waveform. It primarily consists of two phases: 1) With a pair of input and target constructed by adding noise to noisy speech itself for the training of DNN, the first round of training uses noisier speech (noise added to noisy speech) and noisy speech 2) For the following training, using the model trained last time to refine the noisy speech, construct new noisier-noisy pairs for next turn of training. Moreover, to accelerate the process, we apply the iteration into epochs. To evaluate the efficiency, we utilized a dataset including 10 types of real-world noises and made a comparison with two classic supervised and unsupervised methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech enhancement

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A Cost-efficient Hardware Accelerator Design for 2D Sliding Discrete Fourier Transform

A Cost-efficient Hardware Accelerator Design for 2D Sliding ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Juang, Wen-Ho Wu, Meng-Chang Sheu, Yung-Hoh Shieh, Jen-Yu Hsieh, Tung-Hsien National Formosa University Computer Science and Information Engineering Yunlin County Taiwan National Formosa University Department of Electro-Optical Engineering Yunlin County Taiwan National Formosa University Department of Automation Engineering Yunlin County Taiwan

ISBN: (纸本)9798350324174

This work presents a cost-efficient hardware architecture design of 2-D sliding discrete Fourier transform (SDFT). The proposed design requires the lowest eight real adders and six real multipliers in hardware resource, compared with Park's method reduced by 11.1% and 25%, respectively. In the FPGA implementation, the proposed hardware accelerator is operated at 47.47 MHz, and then it is very suitable for time-frequency analysis in real time. © 2023 IEEE.

关键词： Discrete Fourier transforms

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